Highly selective chemical and biological sensors

ABSTRACT

Methods and sensors for selective fluid sensing are provided. Each sensor includes a resonant inductor-capacitor-resistor (LCR) sensor that is coated with a sensing material. In order to collect data, an impedance spectrum is acquired over a relatively narrow frequency range, such as the resonant frequency range of the LCR circuit. A multivariate signature may be calculated from the acquired spectrum to discern the presence of certain fluids and/or fluid mixtures. The presence of fluids is detected by measuring the changes in dielectric, dimensional, resistance, charge transfer, and other changes in the properties of the materials employed by observing the changes in the resonant electronic properties of the circuit. By using a mathematical procedure, such as principal components analysis (PCA) and others, multiple fluids and mixtures can be detected in the presence of one another, even in a high humidity environment or an environment wherein one or more fluids has a substantially higher concentration (e.g. 10×, 1,000,000×) compared to other components in the mixture.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/942,732, entitled “Highly Selective Chemical and Biological Sensors”,filed Nov. 9, 2010, which is herein incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH & DEVELOPMENT

This invention was made with Government support and funded in part bythe National Institute of Environmental Health Sciences under Grant No.1R01ES016569-01A1 and funded in part by the Air Force ResearchLaboratory under Contract No. FS8650-08-C-6869. The Government hascertain rights in the invention.

BACKGROUND

The subject matter disclosed herein relates to chemical and biologicalsensors, and more particularly, to highly selective chemical andbiological sensors.

Chemical and biological sensors are often employed in a number ofapplications were the detection of various vapors maybe used to discernuseful information. For instance, measuring the presence of vapors bydiscerning a change in certain environmental variables within orsurrounding a sensor may be particularly useful in monitoring changes inbiopharmaceutical products, food or beverages, monitoring industrialareas for chemical or physical hazards, as well as in securityapplications such as residential home monitoring, home land security inairports in different environmental and clinical settings and otherpublic venues wherein detection of certain harmful and/or toxic vaporsmay be particularly useful.

One technique for sensing such environmental changes is by employing asensor, such as an RFID sensor, coated with a particular sensingmaterial. Also, sensors maybe arranged in an array of individualtransducers which are coated with sensing materials. Many sensor arraysinclude a number of identical sensors. However, while using identicalsensors simplifies fabrication of the sensor array, such an array mayhave limited capabilities for sensing only a single response (e.g.resistance, current, capacitance, work function, mass, opticalthickness, light intensity, etc). In certain applications multipleresponses or changes in multiple properties may occur. In suchapplications, it may be beneficial to include an array of sensorswherein different transducers in the array employ the same or differentresponses (e.g. resistance, current, capacitance, work function, mass,optical thickness, light intensity, etc.) and are coated with differentsensing materials such that more than one property can be measured.Disadvantageously, fabricating a sensor array having individual sensorsuniquely fabricated to sense a particular response, complicatesfabrication of the array.

Further, in many practical applications, it is beneficial to use highlyselective chemical and biological sensors. That is, it is oftendesirable to provide a sensor array capable of sensing multiple vaporsand vapor mixtures in the presence of other vapors and mixtures. Thegreater the number of vapors and vapor mixtures that may be present, themore difficult it may be to accurately sense and discern a specific typeof vapor or vapor mixture being sensed. This may be particularly truewhen one or more vapors are present at levels of magnitude greater thanthe other vapors of interest for detection. For instance, high humidityenvironments often interfere with the ability of traditional sensors todetect selected vapors.

Various embodiments disclosed herein may address one or more of thechallenges set forth above.

BRIEF DESCRIPTION

In accordance with one embodiment, there is provided a sensor comprisinga resonant inductor-capacitor-resistor (LCR) circuit and a sensingmaterial disposed over the LCR circuit. The sensing material isconfigured to allow selective detection of at least six differentanalyte fluids or gases from an analyzed fluid or gas mixture.

In accordance with another embodiment, there is provided a method ofdetecting analytes in a fluid. The method comprises acquiring animpedance spectrum over a resonant frequency range of a resonant sensorcircuit. The method further comprises calculating a multivariatesignature from the acquired impedance spectrum.

In accordance with another embodiment, there is provided a method ofdetecting chemical or biological species in a fluid. The methodcomprises measuring a real part and an imaginary part of an impedancespectrum of a resonant sensor antenna coated with a sensing material.The method further comprises calculating at least six spectralparameters of the resonant sensor antenna coated with the sensingmaterial. The method further comprises reducing the impedance spectrumto a single data point using multivariate analysis to selectivelyidentify an analyte. The method further comprises determining one ormore environmental parameters from the impedance spectrum.

In accordance with another embodiment, there is provided a sensorcomprising a transducer and a sensing material disposed on thetransducer. The transducer has a multivariate output to independentlydetect effects of different environmental parameters on the sensor. Thesensing material has a preserved magnitude of response to an analyteover a broad concentration range of an interferent.

In accordance with another embodiment, there is provided a method forcontrolling selectivity of a sensor response of a sensor having anintegrated circuit (IC) chip. The method comprises powering the IC chipto at least one power level to affect an impedance spectral profile ofthe sensor. The method further comprises collecting spectral parametersof the sensor response at the at least one power level. The methodfurther comprises performing multivariate analysis of the spectralparameters. The method further comprises calculating values ofenvironmental parameters to which the sensor is exposed from dataproduced by performing the multivariate analysis and using storedcalibration coefficients.

In accordance with another embodiment, there is provided a method forcontrolling selectivity of a sensor response of an LCR sensor having asensing material disposed thereon. The method comprises powering aninductor-resistor-capacitor (LCR) sensor to at least two power levels toaffect at least one of the dipole moment, the dielectric constant, andthe temperature of the sensing material. The method further comprisescollecting spectral parameters of the sensor response at the at leasttwo power levels. The method further comprises performing multivariateanalysis of the spectral parameters from combined impedance spectralprofiles of the LCR sensor at the different power levels. The methodfurther comprises calculating values of environmental parameters towhich the LCR sensor is exposed from data produced by performing themultivariate analysis.

DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates a sensing system, in accordance with embodiments ofthe invention;

FIG. 2 illustrates an RFID sensor, in accordance with embodiments of theinvention;

FIG. 3 illustrates an RFID sensor, in accordance with alternateembodiments of the invention;

FIG. 4 illustrates measured responses of an RFID sensor, in accordancewith embodiments of the invention;

FIGS. 5 and 6 illustrate test data demonstrating an RFID sensor capableof detecting six different vapors, in accordance with embodiments of theinvention;

FIG. 7 illustrates test data demonstrating an RFID sensor capable ofdetecting eight different vapors, in accordance with embodiments of theinvention;

FIG. 8 illustrates test data demonstrating a single sensor capable ofdetermining concentrations of individual vapors in their binary andternary mixtures, in accordance with embodiments of the invention;

FIG. 9 illustrates test data demonstrating a single sensor capable ofdiscriminating between water vapor and nine individual alcohol vaporsfrom their homologous series, in accordance with embodiments of theinvention;

FIG. 10 illustrates comparative plots between the actual and predictedconcentrations of 1-octanol and 1-nonanol, in accordance withembodiments of the invention;

FIG. 11 illustrates tables relating to the comparative plots of FIG. 10,in accordance with embodiments of the invention;

FIGS. 12-14 illustrate test data demonstrating a single sensor capableof highly selective multivariate vapor sensing, in accordance withembodiments of the invention;

FIGS. 15-18 illustrate test data demonstrating independent contactresistance and contact capacitance responses, in accordance withembodiments of the invention;

FIGS. 19-20 illustrates a comparison of conventional impedancespectroscopy and resonant sensing of embodiments having differentdielectric constants; and

FIGS. 21-25 illustrate test data demonstrating improved selectivity ofsensing of vapors of the same dielectric constant using powermodulation.

DETAILED DESCRIPTION

Embodiments disclosed herein provide methods and systems for selectivevapor sensing wherein a single sensor is provided and is capable ofdetecting multiple vapors and/or mixtures of vapors alone, or in thepresence of one another. The disclosed sensors are capable of detectingdifferent vapors and mixtures even in a high humidity environment or anenvironment wherein one or more vapors has a substantially higherconcentration (e.g. 10×) compared to other components in the mixture.Each sensor includes a resonant inductor-capacitor-resistor (LCR) sensorthat is coated with a sensing material. Nonlimiting examples of LCRsensors include RFID sensors with an integrated circuit (IC) memorychip, RFID sensors with an IC chip, and RFID sensors without an ICmemory chip (chipless RFID sensors). LCR sensors can be wireless orwired. In order to collect data, an impedance spectrum is acquired overa relatively narrow frequency range, such as the resonant frequencyrange of the LCR circuit. The technique further includes calculating themultivariate signature from the acquired spectrum and manipulating thedata to discern the presence of certain vapors and/or vapor mixtures.The presence of vapors is detected by measuring the changes indielectric, dimensional, charge transfer, and other changes in theproperties of the materials employed by observing the changes in theresonant electronic properties of the circuit. By using a mathematicalprocedure, such as principal component analysis (PCA) and others,multiple vapors and mixtures can be detected in the presence of oneanother and in the presence of an interferent as further describedbelow. Embodiments disclosed herein provide methods and systems forselective fluid sensing wherein a single sensor is provided and iscapable of detecting multiple fluids and/or mixtures of fluids alone, orin the presence of one another.

To more clearly and concisely describe the subject matter of the claimedinvention, the following definitions are provided for specific terms,which are used in the following description and the appended claims.

The term “fluids” includes gases, vapors, liquids, and solids.

The term “digital ID” includes all data stored in a memory chip of theRFID sensor. Nonlimiting examples of this data are manufactureridentification, electronic pedigree data, user data, and calibrationdata for the sensor.

The term “monitoring process” includes, but is not limited to, measuringphysical changes that occur around the sensor. For example, monitoringprocesses including monitoring changes in a chemical, automotive,biopharmaceutical, food or beverage manufacturing process related tochanges in physical, chemical, and/or biological properties of anenvironment around the sensor. Monitoring processes may also includethose industry processes that monitor physical changes as well aschanges in a component's composition or position. Nonlimiting examplesinclude homeland security monitoring, residential home protectionmonitoring, environmental monitoring, clinical or bedside patientmonitoring, airport security monitoring, admission ticketing, and otherpublic events. Monitoring can be performed when the sensor signal hasreached an appreciably steady state response and/or when the sensor hasa dynamic response. The steady state sensor response is a response fromthe sensor over a determined period of time, where the response does notappreciably change over the measurement time. Thus, measurements ofsteady state sensor response over time produce similar values. Thedynamic sensor response is a response from the sensor upon a change inthe measured environmental parameter (temperature, pressure, chemicalconcentration, biological concentration, etc.) Thus, the dynamic sensorresponse significantly changes over the measurement time to produce adynamic signature of response toward the environmental parameter orparameters measured. Non-limiting examples of the dynamic signature ofthe response include average response slope, average response magnitude,largest positive slope of signal response, largest negative slope ofsignal response, average change in signal response, maximum positivechange in signal response, and maximum negative change in signalresponse. The produced dynamic signature of response can be used tofurther enhance the selectivity of the sensor in dynamic measurements ofindividual vapors and their mixtures. The produced dynamic signature ofresponse can also be used to further optimize the combination of sensingmaterial and transducer geometry to enhance the selectivity of thesensor in dynamic and steady-state measurements of individual vapors andtheir mixtures.

The term “environmental parameters” is used to refer to measurableenvironmental variables within or surrounding a manufacturing ormonitoring system. The measurable environmental variables comprise atleast one of physical, chemical and biological properties and include,but are not limited to, measurement of temperature, pressure, materialconcentration, conductivity, dielectric property, number of dielectric,metallic, chemical, or biological particles in the proximity or incontact with the sensor, dose of ionizing radiation, and lightintensity.

The term “analyte” includes any desired measured environmentalparameter.

The term “interference” includes any undesired environmental parameterthat undesirably affects the accuracy and precision of measurements withthe sensor. The term “interferent” refers to a fluid or an environmentalparameter (that includes, but is not limited to temperature, pressure,light, etc.) that potentially may produce an interference response bythe sensor.

The term “multivariate analysis” refers to a mathematical procedure thatis used to analyze more than one variable from the sensor response andto provide the information about the type of at least one environmentalparameter from the measured sensor spectral parameters and/or toquantitative information about the level of at least one environmentalparameter from the measured sensor spectral parameters. The term“principal components analysis (PCA)” refers to a mathematical procedurethat is used to reduce multidimensional data sets to lower dimensionsfor analysis. Principal component analysis is a part of eigenanalysismethods of statistical analysis of multivariate data and may beperformed using a covariance matrix or correlation matrix. Non-limitingexamples of multivariate analysis tools include canonical correlationanalysis, regression analysis, nonlinear regression analysis, principalcomponents analysis, discriminate function analysis, multidimensionalscaling, linear discriminate analysis, logistic regression, or neuralnetwork analysis.

The term “spectral parameters” is used to refer to measurable variablesof the sensor response. The sensor response is the impedance spectrum ofthe resonance sensor circuit of the LCR or RFID sensor. In addition tomeasuring the impedance spectrum in the form of Z-parameters,S-parameters, and other parameters, the impedance spectrum (its bothreal and imaginary parts) may be analyzed simultaneously using variousparameters for analysis, such as, the frequency of the maximum of thereal part of the impedance (Fp), the magnitude of the real part of theimpedance (Zp), the resonant frequency of the imaginary part of theimpedance (F₁), and the anti-resonant frequency of the imaginary part ofthe impedance (F₂), signal magnitude (Z₁) at the resonant frequency ofthe imaginary part of the impedance (F₁), signal magnitude (Z₂) at theanti-resonant frequency of the imaginary part of the impedance (F₂), andzero-reactance frequency (Fz, frequency at which the imaginary portionof impedance is zero). Other spectral parameters may be simultaneouslymeasured using the entire impedance spectra, for example, quality factorof resonance, phase angle, and magnitude of impedance. Collectively,“spectral parameters” calculated from the impedance spectra, are calledhere “features” or “descriptors”. The appropriate selection of featuresis performed from all potential features that can be calculated fromspectra. Multivariable spectral parameters are described in U.S. patentapplication, Ser. No. 12/118,950 entitled “Methods and systems forcalibration of RFID sensors”, which is incorporated herein by reference.

The term “resonance impedance” or “impedance” refers to measured sensorfrequency response around the resonance of the sensor from which thesensor “spectral parameters” are extracted.

The term “protecting material” includes, but is not limited to,materials on the LCR or RFID sensor that protect the sensor from anunintended mechanical, physical or chemical effect while stillpermitting the anticipated measurements to be performed. For example, ananticipated measurement may include solution conductivity measurementwherein a protecting film separates the sensor from the liquid solutionyet allows an electromagnetic field to penetrate into solution. Anexample of a protecting material is a paper film that is applied on topof the sensor to protect the sensor from mechanical damage and abrasion.Another non-limiting example of a protecting material is a polymer filmthat is applied on top of the sensor to protect the sensor fromcorrosion when placed in a liquid for measurements. A protectingmaterial may also be a polymer film that is applied on top of the sensorfor protection from shortening of the sensor's antenna circuit whenplaced in a conducting liquid for measurements. Non-limiting examples ofprotecting films are paper, polymeric, and inorganic films such aspolyesters, polypropylene, polyethylene, polyethers, polycarbonate,polyethylene terepthalate, zeolites, metal-organic frameworks, andcavitands. The protecting material can be arranged between thetransducer and sensing film to protect the transducer. The protectingmaterial can be arranged on top of the sensing film which is itself ison top of the transducer to protect the sensing film and transducer. Theprotecting material on top of the sensing film which is itself is on topof the transducer can serve to as a filter material to protect thesensing film from exposure to gaseous or ionic interferences.Nonlimiting examples of filter materials include zeolites, metal-organicframeworks, and cavitands.

As used herein the term “sensing materials and sensing films” includes,but is not limited to, materials deposited onto a transducer'selectronics module, such as LCR circuit components, an RFID tag, toperform the function of predictably and reproducibly affecting theimpedance sensor response upon interaction with the environment. Forexample, a conducting polymer such as polyaniline changes itsconductivity upon exposure to solutions of different pH. When such apolyaniline film is deposited onto the LCR or RFID sensor, the impedancesensor response changes as a function of pH. Thus, such an LCR or RFIDsensor works as a pH sensor. When such a polyaniline film is depositedonto the LCR or RFID sensor for detection in gas phase, the impedancesensor response also changes upon exposure to basic (for example, NH₃)or acidic (for example HCl) gases. Alternatively, the sensing film maybe a dielectric polymer. Sensor films include, but are not limited to,polymer, organic, inorganic, biological, composite, and nano-compositefilms that change their electrical and or dielectric property based onthe environment that they are placed in. Non-limiting additionalexamples of sensor films may be a sulfonated polymer such as Nafion, anadhesive polymer such as silicone adhesive, an inorganic film such assol-gel film, a composite film such as carbon black—polyisobutylenefilm, a nanocomposite film such as carbon nanotube-Nafion film, goldnanoparticle-polymer film, metal nanoparticle-polymer film, electrospunpolymer nanofibers, electrospun inorganic nanofibers, electrospuncomposite nanofibers, or films/fibers doped with organic, metallorganicor biologically derived molecules and any other sensing material. Inorder to prevent the material in the sensor film from leaking into theliquid environment, the sensing materials are attached to the sensorsurface using standard techniques, such as covalent bonding,electrostatic bonding and other standard techniques known to those ofordinary skill in the art.

The terms “transducer and sensor” are used to refer to electronicdevices such as RFID devices intended for sensing. “Transducer” is adevice before it is coated with a sensing or protecting film or beforeit is calibrated for sensing application. “Sensor” is a device typicallyafter it is coated with a sensing or protecting film and after beingcalibrated for sensing application.

As used herein the term “RFID tag” refers to an identification andreporting technology that uses electronic tags for indentifying and/ortracking articles to which the RFID tag may be attached. An RFID tagtypically includes at least two components where the first component isan integrated circuit (IC) memory chip for storing and processinginformation and modulating and demodulating a radio frequency signal.This memory chip can also be used for other specialized functions, forexample it can contain a capacitor. It can also contain at least oneinput for an analog signal such as resistance input, capacitance input,or inductance input. In the case of a chipless RFID tag, the RFID tagmay not include an IC memory chip. This type of RFID tag may be usefulin applications where a specific RFID tag does not need to beidentified, but rather a signal merely indicating the presence of thetag provides useful information (e.g., product security applications).The second component of the RFID tag is an antenna for receiving andtransmitting the radio frequency signal.

The term “RFID sensor” is an RFID tag with an added sensing function as,for example, when an antenna of the RFID tag also performs sensingfunctions by changing its impedance parameters as a function ofenvironmental changes. The accurate determinations of environmentalchanges with such RFID sensors are performed by analysis of resonanceimpedance. For example, RFID tags may be converted into RFID sensors bycoating the RFID tag with a sensing film. By coating the RFID tag with asensing film, the electrical response of the film is translated intosimultaneous changes to the complex impedance response, resonance peakposition, peak width, peak height and peak symmetry of the impedanceresponse of the sensor antenna, magnitude of the real part of theimpedance, resonant frequency of the imaginary part of the impedance,antiresonant frequency of the imaginary part of the impedance,zero-reactance frequency, phase angle, and magnitude of impedance, andothers as described in the definition of the term sensor “spectralparameters”. The “RFID sensor” can have an integrated circuit (IC)memory chip attached to antenna or can have no IC memory chip. An RFIDsensor without an IC memory chip is an LCR sensor. An LCR sensor iscomprised of known components such as at least one inductor (L), atleast one capacitor (C), and at least one resistor (R) to form an LCRcircuit.

The term “single-use container” includes, but is not limited to,manufacturing or monitoring equipment, and packaging, which may bedisposed of after use or reconditioned for reuse. Single-use packagingin the food industry includes but is not limited to food and drinkspackaging, candy and confection boxes. Single-use monitoring componentsinclude, but are not limited to, single-use cartridges, dosimeters, andcollectors. Single use manufacturing containers include, but are notlimited to, single-use vessels, bags, chambers, tubing, connectors, andcolumns.

The term “writer/reader” includes, but is not limited to, a combinationof devices to write and read data into the memory of the memory chip andto read impedance of the antenna. Another term for “writer/reader” is“interrogator”.

In accordance with embodiments disclosed herein, an LCR or an RFIDsensor for sensing vapors, vapor mixtures, fluids, fluid mixtures andbiological species is described. As previously described, the RFIDsensor includes an RFID tag coated with a sensing material. In oneembodiment, a passive RFID tag may be employed. As will be appreciated,an RFID tag may include an IC memory chip, which is connected to anantenna coil for communication with a writer/reader. The IC memory chipcan be read by illuminating the tag by a radio frequency (RF) and/ormicrowave carrier signal sent by the writer/reader. When the RF and/ormicrowave field passes through an antenna coil, an AC voltage isgenerated across the coil. The voltage is rectified in the microchip toresult in a DC voltage for the microchip operation. The IC memory chipbecomes functional when the DC voltage reaches a predetermined level. Bydetecting the RF and/or microwave signal backscattered from themicrochip, the information stored in the microchip can be fullyidentified. The distance between the RFID tag/sensor and thewriter/reader is governed by the design parameters that includeoperating frequency, RF and/or microwave power level, the receivingsensitivity of the reader/writer, antenna dimensions, data rate,communication protocol, and microchip power requirements.

In one embodiment a passive RFID tag with or without an IC memory chipmay be employed. Advantageously, a passive RFID tag does not rely on abattery for operation. The typical frequency range of operation of 13.56MHz passive RFID tags for digital ID writing/reading is from 13.553 to13.567 MHz. The typical frequency range of operation of 13.56-MHzpassive RFID sensors for sensing of environmental changes around theRFID sensor is from about 5 MHz to about 20 MHz, more preferably from 10to 15 MHz. The requirement for this frequency range is to be able torecognize the tag with a writer/reader that operates at 13.56 MHz whilethe sensor portion of the RFID tag operates from 5 to 20 MHz.

Depositing sensing films onto passive RFID tags creates RFID chemical,biological, or physical sensors. RFID sensing is performed by measuringchanges in the RFID sensor's impedance as a function of physical changesaround the sensor, as described further below. Examples of physicalchanges include, but are not limited to, temperature, pressure,conductivity, and dielectric properties. If the frequency response ofthe antenna coil, after deposition of the sensing film, does not exceedthe frequency range of operation of the tag, the information stored inthe microchip can be identified with a conventional RFID writer/reader.Similarly, an impedance analyzer (sensor reader) can read the impedanceof the antenna coil to correlate the changes in impedance to thechemical and biological species of interest and to physical, chemical,or/and biological changes of environmental parameters around the sensor.

In operation, after coating of the RFID tag with a chemically sensitivefilm, both the digital tag ID and the impedance of the tag antenna maybe measured. The measured digital ID provides information about theidentity of the tag itself, such as an object onto which this tag isattached, and the properties of the sensor (e.g. calibration curves fordifferent conditions, manufacturing parameters, expiration date, etc.).For multi-component detection, multiple properties from the measuredreal and imaginary portions of the impedance of a single RFID sensor maybe determined, as described further below.

In alternate embodiments, the selective sensor performance can beachieved not only by using a sensing material deposited onto thetransducer, but also by depositing a protective film onto thetransducer, or using the bare transducer itself.

In accordance with the embodiments described herein, in order to achievehigh selectivity detection of analytes in the presence of high levels ofinterferences, the sensor should exhibit a number of characteristics.First, the selected transducer should include a multivariate output toindependently detect the effects of different environmental parameterson the sensor. Second, the sensing material should have a preservedmagnitude of response to an analyte over a wide concentration range ofan interferent. The response to the relatively small analyteconcentrations should not be fully suppressed by the presence of therelatively high concentrations of the interferents. Third, the responseof the sensing material to interference species is allowed and may existbut should not compete with the response to the analyte and should be ina different direction of the multivariate output response of thetransducer.

To achieve these characteristics, in one embodiment, the sensingmaterial has multiple response mechanisms to fluids where these responsemechanisms are related to the changes of dielectric constant,resistance, and swelling of the sensing material where these changes arenot fully correlated with each other and produce different patterns uponexposure to individual vapors and their mixtures. Further, the LCRtransducer can have multiple components of LCR response from the LCRcircuit where these multiple components of LCR response originate fromthe different factors affecting the transducer circuit with thenonlimiting examples that include material resistance and capacitance,contact resistance and capacitance between the transducer and sensingmaterial, resistance and capacitance between the transducer substrateand sensing material. Further, the LCR transducer can have multipleconditions of LCR circuit operation where an integrated circuit chip isa part of the sensor circuit.

Thus, one method for controlling the selectivity of the sensor responseinvolves powering of the integrated circuit chip to affect the impedancespectral profile. The different impedance spectral profiles change theselectivity of sensor response upon interactions with different fluids.The IC chip or IC memory chip on the resonant antenna contains arectifier diode and it can be powered at different power levels toinfluence the impedance spectral profile of the sensor. The differencesin spectral profiles at different power levels are pronounced indifferent values of Fp, F1, F2, Fz, Zp, Z1, Z2, and calculated values ofC and R. In one embodiment, the enhanced sensor selectivity is achievedthrough the appropriate selection of at least one power level of the ICchip or IC memory chip operation. In another embodiment, the enhancedsensor selectivity is achieved through the appropriate selection of atleast two power levels of the IC chip or IC memory chip operation andanalyzing the combined impedance spectral profiles of the sensor underdifferent power levels. Powering of the sensor with at least two powerlevels is performed in the alternating fashion between a relatively lowand relatively high power. The alternating powering of the sensor withat least two power levels is performed on the time scale which is atleast 5 times faster than the dynamic changes in the measuredenvironmental parameters. In all these embodiments, powering atdifferent power levels is in the range from −50 dBm to +40 dBm andprovides the ability to detect more selectively more analytes and/or toreject more selectively more interferences.

Another method of controlling the selectivity of the sensor responseinvolves applying different powers to the LCR or to RFID sensor toaffect the dipole moment, the dielectric constant, and/or temperature ofthe material in proximity to the sensor. The material in proximity tothe sensor refers to the sensing material deposited onto the sensorand/or the fluid under investigation. These changes in the dipolemoment, the dielectric constant, and/or temperature of the material inproximity to the sensor when exposed to different power levels of LCR orRFID sensor operation originate from the interactions of theelectromagnetic field with these materials. Powering of the sensor withat least two power levels is performed in the alternating fashionbetween a relatively low and relatively high power. The alternatingpowering of the sensor with at least two power levels is performed onthe time scale which is at least 5 times faster than the dynamic changesin the measured environmental parameters. In all these embodiments,powering at different power levels is in the range from −50 dBm to +40dBm and provides the ability to detect more selectively more analytesand/or to reject more selectively more interferences. Operation at aselected power or at multiple powers results in selective detection offluids with the same dielectric constant.

Turning now to the figures and referring initially to FIG. 1, a sensingsystem 10 is provided to illustrate the principle of selective vaporsensing utilizing an RFID sensor 12 having a sensing material 14, coatedthereon. Referring briefly to FIG. 2, the sensor 12 is a resonantcircuit that includes an inductor-capacitor-resistor structure (LCR)coated with the sensing material 14. The sensing material 14 is appliedonto the sensing region between the electrodes, which form sensorantenna 18 that constitute the resonant circuit. As will be describedfurther below, by applying the sensing material 14 onto the resonantcircuit, the impedance response of the circuit will be altered. Thesensor 12 may be a wired sensor or a wireless sensor. The sensor 12 mayalso include a memory chip 16 coupled to resonant antenna 18 that iscoupled to a substrate 20. The memory chip 16 may include manufacturing,user, calibration and/or other data stored thereon. The memory chip 16is an integrated circuit device and it includes RF signal modulationcircuitry fabricated using a complementary metal-oxide semiconductor(CMOS) process and a non-volatile memory. The RF signal modulationcircuitry components include a diode rectifier, a power supply voltagecontrol, a modulator, a demodulator, a clock generator, and othercomponents.

FIG. 3 illustrates an alternative embodiment of the sensor 12,designated by reference numeral 21, wherein a complementary sensor 23comprising a sensing material 14 is attached across the antenna 18 andthe integrated circuit (IC) memory chip 16 to alter the sensor impedanceresponse. In another embodiment (not illustrated), a complementarysensor may be attached across an antenna that does not have an IC memorychip and alters sensor impedance response. Nonlimiting examples ofcomplementary sensors are interdigitated sensors, resistive sensors, andcapacitive sensors. Complementary sensors are described in U.S. patentapplication, Ser. No. 12/118,950 entitled “Methods and systems forcalibration of RFID sensors”, which is incorporated herein by reference.

In one embodiment, a 13.56 MHz RFID tag may be employed. Duringoperation of the sensing system 10, the impedance Z(ƒ) of the sensorantenna 18 and the digital sensor calibration parameters stored on thememory chip 16 may be acquired. Referring again to FIGS. 2 and 3,measurement of the resonance impedance Z(ƒ) of the antenna 18 and thereading/writing of digital data from the memory chip 16 are performedvia mutual inductance coupling between the RFID sensor antenna 18 andthe pickup coil 22 of a reader 24. As illustrated, the reader 24 mayinclude an RFID sensor impedance reader 26 and an integrated circuitmemory chip reader 28. The interaction between the RFID sensor 12 andthe pickup coil 22 can be described using a general mutual inductancecoupling circuit model. The model includes an intrinsic impedance Z_(C)of the pickup coil 22 and an intrinsic impedance Z_(S) of the sensor 12.The mutual inductance coupling M and the intrinsic impedances Z_(C) andZ_(S) are related through the total measured impedance Z_(T) across theterminal of the pickup coil 22, as represented by the followingequation:

Z _(T) =Z _(C)+(ω² M ² /Z _(S)),  (1)

-   wherein ω is the radian carrier frequency and M is the mutual    inductance coupling coefficient.

Sensing is performed via monitoring of the changes in the properties ofthe sensing material 14 as probed by the electromagnetic field generatedin the antenna 18 (FIG. 2). Upon reading the RFID sensor 12 with thepickup coil 22, the electromagnetic field generated in the sensorantenna 18 extends out from the plane of the sensor 12 and is affectedby the dielectric property of an ambient environment providing theopportunity for measurements of physical, chemical, and biologicalparameters. For measurements of highly conducting species (liquids orsolids), the protecting or sensing material 14 provides a protectivebarrier that separates the conducting medium from the resonant antenna.For measurement in highly conducting media, the protecting or sensingmaterial 14 prevents the RFID tag from direct contact with the liquidand loss of the sensor resonance. For measurements of low conductingmedia (e.g., approximately 0.5 μS/cm), the sensor can operate andperform measurements without a protecting material.

Similarly, sensing is performed via monitoring of the changes in theproperties of the sensing material 14 as probed by the electromagneticfield generated in the complementary sensor 23 (FIG. 3). Upon readingthe RFID sensor 21 with the pickup coil 22, the electromagnetic fieldgenerated in the complementary sensor 23 extends out from the plane ofthe complementary sensor 23 and is affected by the dielectric propertyof an ambient environment providing the opportunity for measurements ofphysical, chemical, and biological parameters. For measurements ofhighly conducting species (liquids or solids), the protecting or sensingmaterial 14 provides a protective barrier that separates the conductingmedium from the resonant antenna. For measurement in highly conductingmedia, the protecting or sensing material 14 prevents the RFID tag fromdirect contact with the liquid and loss of the sensor resonance. Formeasurements of low conducting media, the sensor can operate and performmeasurements without a protecting material.

FIG. 4 illustrates an example of measured responses of an exemplary RFIDsensors 12 and 21, in accordance with embodiments of the invention,which includes the sensor's full impedance spectra and severalindividually measured spectral parameters. To selectively detect severalvapors or fluids using a single RFID sensor, such as the RFID sensors 12and 21, the real Z_(re)(ƒ) and imaginary Z_(im)(ƒ) parts of theimpedance spectra Z(ƒ)=Zre(ƒ)+jZ_(im)(ƒ) are measured from the sensors12 and 21 coated with a sensing material and at least four spectralparameters are calculated from the measured Z_(re)(ƒ) and Z_(im)(ƒ), asillustrated in the plot 30 of FIG. 4. Seven spectral parameters can becalculated as illustrated in the plot 30 of FIG. 4. These parametersinclude the frequency position Fp and magnitude Zp of Z_(re)(ƒ), theresonant F1 and anti-resonant F2 frequencies of Z_(im)(ƒ), the impedancemagnitudes Z1 and Z2 at F1 and F2 frequencies, respectively, and thezero-reactance frequency FZ. Additional parameters, such as qualityfactor may also be calculated. From the measured parameters, resistanceR, capacitance C, and other parameters of the sensors 12 and 21 can bealso determined Multivariate analysis may be used to reduce thedimensionality of the impedance response, either from the measured realZ_(re)(ƒ) and imaginary Z_(im)(ƒ) parts of the impedance spectra or fromthe calculated parameters Fp, Zp, F1 and F2, and possibly otherparameters to a single data point in multi-dimensional space forselective quantization of different vapors or fluids, as will beappreciated by those skilled in the art, and as will be describedfurther below.

The presence of even relatively low levels of interferences (0.1-10 foldoverloading levels) represents a significant limitation for individualsensors due to their insufficient selectivity. This problem can beaddressed with an introduction of a concept of sensor arrays.Unfortunately, in practical situations (e.g. urban, environmental, andworkplace monitoring, breath analysis, and others), sensor arrays sufferfrom interference effects at high (10²-10⁶ fold) overloading levels.These interference effects reduce the use of both, sensors and sensorarrays. Advantageously, embodiments described herein provide techniquesto overcome these two key scientific limitations of existing sensors andsensor arrays, such as difficulty or inability of operating with highoverloading from interferences and of selective measurements of multiplevapors and their mixtures using a single sensor.

The well-accepted limitations of impedance spectroscopy in practicalsensors for trace analyte detection include relatively low sensitivityand prohibitively long acquisition times over the broad frequency range.Embodiments described herein enhance the ability to measure changes inproperties of the sensing material by putting the material onto theelectrodes of the resonant LCR sensor circuit. Similarly, the disclosedembodiments enhance the ability to measure changes in properties of thefluid in proximity to the the electrodes of the resonant LCR sensorcircuit. Experimental testing examined the effects of changingdielectric constant on sensing electrodes both with and without aresonator. Compared to the conventional impedance spectroscopy, the bareresonant LCR sensor provided an at least 100-fold enhancement in thesignal-to-noise (SNR) over the smallest measured range of the dielectricconstant difference (Δε) with the corresponding improvement of detectionlimit of dielectric constant determinations.

Performance of the LCR sensor as analyzed using multivariate analysistools provides an advantage of improved selectivity over the processingof individual responses of individual sensors. In particular, testresults indicate the relations between Fp and Zp and the relationsbetween calculated sensor resistance R and calculated sensor capacitanceC have a much less selectivity between responses to different vapors orfluids as compared to the relations between multivariable parameterssuch as PC1 and PC2 and others. Further, the LCR sensors demonstrateindependent contact resistance and contact capacitance responses thatimprove the overall selectivity of the multivariable response of the LCRsensors. This selectivity improvement originates from the independentcontributions of the contact resistance and contact capacitanceresponses to the equivalent circuit response of the sensor.

Diverse sensing materials may be advantageously utilized on the sensingregion of the LCR resonant sensor because analyte-induced changes in thesensing material film affect the impedance of the antenna LCR circuitthrough the changes in material resistance and capacitance, contactresistance and capacitance between the transducer and sensing material,resistance and capacitance between the transducer substrate and sensingmaterial. Such changes provide diversity in response of an individualRFID sensor and provide the opportunity to replace a whole array ofconventional sensors with a single LCR or RFID sensor.

Sensing films for the disclosed LCR and RFID sensors may include avariety of materials provided the environmental changes are detectableby changes in resonant LCR circuit parameters. Non-limiting examples ofpossible sensing film materials are a hydrogel such aspoly(2-hydroxyethyl methacrylate), a sulfonated polymer such as Nafion,an adhesive polymer such as silicone adhesive, an inorganic film such assol-gel film, a biological-containing film such as DNA, antibody,peptide or other biomolecules deposited as a film, abiological-containing film such as DNA, antibody, enzyme, peptide,polysaccharide, protein, aptamer, or other biomolecules or viruses,spores, cells, deposited as a part of a inorganic or polymeric film, acomposite film, a nanocomposite film, functionalized carbon nanotubefilm, or film made of surface functionalized gold nanoparticles,electrospun polymeric, inorganic, and composite nanofibers, andnanoparticles that have one dielectric property and incorporated in amatrix that have another dielectric property.

Sensing materials can be selected to have different dielectric constantsranging from about 2 to about 40. Nonlimiting examples includepolyisobutylene (PIB, ε′_(r)=2.1), ethyl cellulose (EC, ε′_(r)=3.4),polyepichlorihydrin (PECH, ε′_(r)=7.4), cyanopropyl methyl phenylmethylsilicone (OV-225, ε′_(r)=11), dicyanoallyl silicone (OV-275, ε′_(r)=33).The use of these materials provides the ability to tailor the relativedirection of sensing response upon exposure to vapors of differentdielectric constant. The different partition coefficients of vapors intothese or other sensing materials further modulate the diversity andrelative direction of the response.

“Composites” are materials made from two or more constituent materialswith significantly different physical or chemical properties, whichremain separate and distinct on a macroscopic level within the finishedstructure. Nonlimiting examples of composites include carbon blackcomposites with poly(4-vinylphenol), poly(styrene-co-allyl alcohol),poly(vinyl chloride-covinyl acetate), and other materials.“Nanocomposites” are materials made from two or more constituentmaterials with significantly different physical or chemical properties,which remain separate and distinct on a nanoscale level within thefinished structure. Nonlimiting examples of nanocomposites include:carbon nanotube nanocomposites with polymers (such aspoly(N-vinylpyrrolidone), polycarbonate, polystyrene, etc.);semiconducting nanocrystal quantum dot nanocomposites with polymers,metal oxide nanowires, and carbon nanotubes; metal nanoparticles ornanoclusters functionalized with carbon nanotubes.

Sensing materials exhibit analyte responses which can be described byone or more of three response mechanisms of LCR or RFID sensors such asresistance changes, dielectric constant changes and swelling changes. Acomposite sensing material can be constructed which incorporate multipledifferent individual sensing materials which each respond to analytes bypredominantly different response mechanisms. Such composite sensingmaterial produces an enhanced diversity in the multivariate response.Such composite sensing materials may be homogeneously or inhomogeneouslymixed or locally patterned over specific portions of the LCR resonator.

For example, a wide range of metal oxide semiconductor materials (e.g.ZnO, TiO₂, SrTiO₃, LaFeO₃, etc) exhibit changes in resistance uponexposure to analyte gases, but some mixed metal oxides (e.g. CuO—BaTiO₃,ZnO—WO₃) change their permittivity/capacitance upon exposure to analytevapors. By combining these materials either as mixtures, or by spatiallyseparated deposition onto the same sensor, their separate contributionsto the local environment surrounding the sensor are used to enhance thediversity of response mechanisms for a single analyte, thus enhancingselectivity.

As a further example, ligand-coated conducting (e.g. metal)nanoparticles are used as vapor and fluid sensing materials because oftheir strong changes in resistance due to localized swelling induced byanalyte adsorption into the ligand shell and the subsequent change intunneling efficiency between neighboring conducting nanoparticles anddielectric constant changes of the environment between these conductingnanoparticles. In combination with a dielectric polymer (nonlimitingexamples include silicones, poly(etherurethane), polyisobutylenesiloxane fluoroalcohol, etc.), conjugated polymer (polyaniline,polythiophene, poly(vinyl ferrocene), poly(fluorene)-diphenylpropane),poly(3,4-ethylenedioxythiophene) polypyrrole, bilypyrrole) or any othermaterial (nonlimiting examples include porphyrins, metalloporphyrins,metallophthalocyanines, carbon nanotubes, semiconducting nanocrystals,metal oxide nanowires) that responds to analyte adsorption with morepronounced changes in capacitance or resistance, a sensor with a widerrange of analyte responses is developed.

Further, in order to avoid potentially deleterious effects of disparatematerials on each other in a composite sensing material (e.g. highdielectric constant medium suppressing conduction in a conductive fillermaterial), this material components are chosen to locally phase separatedue to hydrophylic/hydrophobic interactions or mutual immiscibility,allowing the different mechanisms active in each component to be sensedby the sensor. In another embodiment, a composite sensing material canbe formed as sectors of individual materials deposited adjacent to eachother onto a single sensor. In another embodiment, a composite sensingmaterial can be formed as layers of individual materials deposited ontop of each other onto a single sensor.

In certain embodiments, sensing materials may be porphyrins,metalloporphyrins, metallophthalocyanines, and related macrocycles. Inthese materials, gas sensing is accomplished either by π-stacking of thegas into organized layers of the flat macrocycles or by gas coordinationto the metal center without the cavity inclusion. Metalloporphyrinsprovide several mechanisms of gas response including hydrogen bonding,polarization, polarity interactions, metal center coordinationinteractions and molecular arrangements. Molecules of porphyrins,metalloporphyrins, metallophthalocyanines, and related macrocycles canbe also assembled into nanostructures.

Further types of materials include aligned nanostructures wherealignment is performed by various known methods (dielectrophoreticalignment, alignment during material polymerization, alignment due tospatial confinement, alignment during slow solvent evaporation, andothers), self-assembled structures such as colloidal crystal structuresof the same size of particles, multilayers of colloidal crystal filmswhere different layers have different size of assembled particles,nanoparticle assemblies where the particles have core-shell structurewith the particle core of one dielectric property and particle shell ofanother dielectric property, bio-inspired materials, zero-dimensionalnanomaterials, one-dimensional nanomaterials, two-dimensionalnanomaterials, and three-dimensional nanomaterials.

Self-assembled structures include colloidal crystal structures of thesame size of particles, multilayers of colloidal crystal films wheredifferent layers have different sizes of assembled particles,nanoparticle assemblies where the particles have core-shell structurewith the particle core of one dielectric property and particle shell ofanother dielectric property. Nonlimiting examples of materials ofself-assembled colloidal crystal structures include polystyrene,polymethylmethacrylate, polyvinyltoluene, styrene/butadiene copolymers,styrene/vinyltoluene copolymers, and silica. The typical diameters ofthese colloidal particles depend on the type of material and may rangefrom 50 nanometers to 25 micrometers. Nonlimiting examples of colloidalcrystal structures with multiple layers include at least one layer ofparticles of one size assembled as a colloidal array onto the sensorsubstrate and at least one layer of particles of another size assembledas a colloidal array on top of the previous layer. Nonlimiting examplesof bio-inspired materials include super hydrophobic or superhydrophilicmaterials.

Nonlimiting examples of zero-dimensional nanomaterials include metalnanoparticles, dielectric nanoparticles, core-shell nanoparticles, andsemiconducting nanocrystals. Nonlimiting examples of one-dimensionalnanomaterials include nanotubes, nanowires, nanorods, and nanofibers.Nonlimiting examples of two-dimensional nanomaterials include graphene.Nonlimiting examples of three-dimensional nanomaterials include selfassembled films of several layers of colloidal spheres.

Nonlimiting examples of nanoparticles that have core-shell structurewith the particle core of one dielectric property and particle shell ofanother dielectric property include: metal (gold, silver, their alloy,etc.) core nanoparticles and organic shell layers (dodecanethiol,decanethiol, 1-butanethiol, 2-ethylhexanethiol, hexanethiol,tert-dodecanethiol, 4-methoxy-toluenethiol, 2-mercaptobenzoxazole,11-mercapto-1-undecanol, 6-hydroxyhexanethiol); polymeric core(polystyrene, polymethylmethacrylate) and inorganic shell (silica);isolating core (polystyrene, polymethylmethacrylate, silica) andsemiconducting shell (carbon nanotubes, TiO2, ZnO, SnO2, WO3), andcarbon nanotube core that is decorated with metal nanoparticles. Thenanoparticles of metal (gold, silver, their alloy, etc.) corenanoparticles and organic shell layers can be further modified withorganic and polymeric molecules. Nonlimiting example of organicmolecules include porphyrins, metalloporphyrins, metallophthalocyanines,and macrocycles, cavitands, surpamolecular compounds. Nonlimitingexample of polymeric molecules include polymeric molecules withdifferent dielectric constants ranging from 2 to 40. Nonlimitingexamples include polyisobutylene (PIB, ε′_(r)=2.1), ethyl cellulose (EC,ε_(r)=3.4), polyepichlorihydrin (PECH, ε′_(r)=7.4), cyanopropyl methylphenylmethyl silicone (OV-225, ε′_(r)=11), dicyanoallyl silicone(OV-275, ε′_(r)=33). A nonlimiting example of fabrication of thesesensing materials involves (1) preparation of metal core nanoparticleswith an organic shell in a solvent, (2) mixing this composition withanother composition of polymeric or organic molecules in a solvent, and(3) depositing a sensing film on an LCR or RFID transducer from thiscombined mixture. The use of these materials in combination with metalcore nanoparticles provides the ability to tailor the relative directionof sensing response upon exposure to vapors of different dielectricconstant. The different partition coefficients of vapors into these orother sensing materials further modulate the diversity and relativedirection of the response.

Other sensing materials include semiconducting metal oxides, zeolites,cavitands, ionic liquids, liquid crystals, crown ethers, enzymes,polysilsesquioxanes, metal-organic frameworks (MOFs).

Other sensing materials include synthetic dielectric and conductingpolymers with different polymer side group functionalities, anddifferent polymer formulations; biomolecules for gas-phase sensing;cavitands with dominating intracavity complexation and a totallysuppressed non specific extracavity adsorption of vapors provided bycavitand deposition; porphyrins and related molecules as individualmolecules and as assembled into polymers and nanostructures.

To further improve selectivity of response, overcoating of sensing filmswith auxiliary membrane filter films may be performed. Nonlimitingexamples of these filter films include zeolite, metal-organic framework,and cavitand filters.

These diverse sensing materials shown as nonlimiting examples areprovided on the sensing region of the LCR or RFID resonant sensorbecause analyte-induced changes in the sensing material film affect thecomplex impedance of the antenna LCR circuit through the changes inmaterial resistance and capacitance, contact resistance and capacitancebetween the transducer and sensing material, resistance and capacitancebetween the transducer substrate and sensing material. Such changesprovide diversity in response of an individual RFID sensor and providethe opportunity to replace a whole array of conventional sensors with asingle LCR or RFID sensor, as illustrated further below, with regard toEXPERIMENTAL DATA.

Experimental Data

Resonant antenna structures, such as those described above, were usedfor demonstration of the disclosed techniques. Various sensing materialswere applied onto the resonant antennas by conventional draw-coating,drop coating, and spraying processes. Measurements of the impedance ofthe RFID and LCR sensors were performed for example with a networkanalyzer (Model E5062A, Agilent Technologies, Inc. Santa Clara, Calif.)under computer control using LabVIEW. The network analyzer was used toscan the frequencies over the range of interest (i.e., the resonantfrequency range of the LCR circuit) and to collect the impedanceresponse from the RFID and LCR sensors.

For gas sensing, different concentrations of vapors were generated usingan in-house built computer-controlled vapor-generation system. Collectedimpedance data was analyzed using KaleidaGraph (Synergy Software,Reading, Pa.) and PLS_Toolbox (Eigenvector Research, Inc., Manson,Wash.) operated with Matlab (The Mathworks Inc., Natick, Mass.).

EXAMPLE 1 Selective Detection of Six Vapors with a Single Sensor

As illustrated in FIGS. 5 and 6, test results were obtained todemonstrate the selective detection of six different vapors, using asingle sensor, such as the sensor 12 described above. As illustrated inFIG. 5, the sensor was exposed to the following 10 vapors over a periodof time:

1 water 2 methanol 3 acetonitrile 4 acetone 5 isopropyl alcohol 6toluene 7 chloroform 8 chlorobenzene 9 trichloroethylene 10 benzene

The sensing material used to coat the RFID tag was carefully chosen andprovided the ability to selectively detect at least six of the listedvapors. In the present experiment, the chosen sensing material waspoly(etherurethane) (PEUT) dissolved in a nonpolar solvent such asdichloromethane. During the experiment, the RFID sensor wasincrementally exposed to 10 vapors over a period of time. The test wasconducted in steps, where the concentration of each respective vapor wasincreased with each step. By monitoring changes in certain propertiesand examining various responses over time and at increasingconcentration levels, the data demonstrated the ability to distinguishsix of the 10 vapors tested in the above-described experiment.

For instance, the frequency position Fp and magnitude Zp of the realpart of the total resistance Z_(re)(ƒ), as well as the capacitance C,are illustrated in FIG. 5, as response plots 44, 40 and 42,respectively. The tests for each vapor were conducted and plotted over 4increments of increasing concentration, as clearly indicated by thestepped nature of the response for each vapor. For example, referring tothe plot of the magnitude Zp, the magnitude Zp for each vapor (1-10)exhibits four steps, correlative to the increases in concentration ofeach vapor over time. From examining this plot alone, certain of thevapors can clearly be distinguished from one another. By way of example,the magnitude Zp response for chloroform (7) is very strong, and itnotably discernable from each of the other responses. Accordingly, theexemplary RFID sensor is able to selectively detect chloroform (7). Incontrast, when viewing the magnitude Zp response of methanol (2), itappears very similar to the magnitude Zp of acetone (4). Based solely onthe magnitude Zp response, the exemplary RFID sensor may not be suitablefor detecting and distinguishing between these two vapors.

However, as previously described, a number of other responses (e.g. thefrequency position Fp and the capacitance C) may also be analyzed andmay provide further information that may be manipulated and analyzed inorder to provide a way to distinguish vapors, wherein one particularresponse may not be sufficient. Referring to the test data for frequencyposition Fp response plot 44, the frequency position Fp of methanol (2)is distinguishable from the frequency position Fp of acetone (4).Accordingly, the exemplary RFID sensor may be sufficient fordistinguishing such vapors, when other responses, such as the frequencyposition Fp (as opposed to the magnitude Zp response alone), areanalyzed.

One convenient way of analyzing various responses of the sensor is touse principal components analysis (PCA) to produce a multivariatesignature. As will be appreciated, PCA analysis is a mathematicalprocess, known to those skilled in the art, that is used to reducemultidimensional data sets to lower dimensions for analysis. Forinstance, the various responses for each vapor at a given concentrationmay be reduced to a single data point, and from this, a single responsefor each vapor which may be represented as a vector, may be discerned,as illustrated in FIG. 6. FIG. 6 represents a PCA plot 46 of the variousresponses of the 10 vapors described with reference to FIG. 5. As willbe appreciated, PC1 represents the response with the most variation,while PC2 represents the response with the next most variation. Asillustrated, the vectors for acetone (4) and trichloroethylene (9) maybe difficult to distinguish from one another. Similarly, the vectors fortoluene (6) and benzene (10) may be difficult to distinguish from oneanother. However, the remaining six vapors are clearly distinguishablefrom one another. Accordingly, the instant test data provides supportfor a sensor capable of discerning between at least six vapors, herewater (1), methanol (2), acetonitrile (3), isopropyl alcohol (5),chloroform (7), and chlorobenzene (8).

In addition, vapor mixtures may also be discernable from the PCA plot.For instance, one may be able to extrapolate a vector plot of a mixtureof acetonitrile (3) and chloroform (7). Such additional extrapolateddata may also be used to selectively detect mixtures of selected vapors.Further, by varying the selected sensing material, even greater numbersof selective vapor detection has been demonstrated, utilizing a singleRFID sensor.

EXAMPLE 2 Selective Detection of Eight Vapors with a Single Sensor

As illustrated in FIG. 7, test results were obtained to demonstrate theselective detection of eight different vapors, using a single sensor,such as the sensor 12 described above. The sensing material used to coatthe RFID tag was carefully chosen and provided the ability toselectively detect the listed vapors. In the present experiment, thechosen sensing material was PEUT dissolved in a nonpolar solvent such asdichloromethane. As previously described, the tests were conducted withincremental increases in concentration. As illustrated in the plot 48 ofFIG. 7, the sensor coated with PEUT was able to discriminate thefollowing 8 vapors:

1 water 2 1-methoxy 2-propanol 3 methyl ethyl ketone 4 acetonitrile 5toluene 6 chloroform 7 tetrahydrofuran 8 dimethylformamide

EXAMPLE 3 Selective Detection of Binary and Ternary Mixtures with aSingle Sensor

Vapors chloroform, tetrahydrofuran (THF), and dimethylformamide (DMF)were further selected for measurements of binary and ternary mixtureswith a single sensor, as described in EXAMPLE 1. Using a singledeveloped sensor, detection of individual vapors in their binary andternary mixtures was demonstrated, as illustrated in FIG. 8. Correlationplots 50, 52 and 54, corresponding to chloroform, THF and DMF,respectively, between actual and predicted concentrations of theindividual vapors in their mixtures, had excellent correlationcoefficients. Vapors concentrations were from 0 to 0.15 P/Po where P isthe partial pressure and Po is the saturated vapor pressure. Theseresults demonstrated a unique ability of developed individual sensors toquantify 2-3 vapors in their mixtures. This discrimination has becomepossible because the sensor's multivariable response was modeled usingthe first, second, and third principal components (PCs) of the built PCAmodel.

EXAMPLE 4 Selective Detection of Nine Vapors with a Single Sensor in thePresence of Variable Relative Humidity

In this EXAMPLE, an RFID sensor (as described in EXAMPLE 1) coated withPEUT was also tested and a PCA evaluation demonstrated an RFID sensorcapable of discriminating between up to nine vapors in the presence ofvariable relative humidity. Specifically, ethanol, 1-methoxy 2-propanol,methyl ethyl ketone, acetonitrile, toluene, choloroform, tetrahydrofuran(THF) dimethylformanmide (DMF) and acetone were selectively detected. Incertain embodiments, the PCA analysis data may also be plotted in threedimensions, thereby providing an even greater ability to discriminateamong various vapors.

EXAMPLE 5 Selective Detection of Individual Nine Alcohols with a SingleSensor

With recognition of eight and nine diverse vapors demonstratedimplementing sensors disclosed herein (EXAMPLEs 2 and 4, respectively),further testing was conducted to demonstrate selective detecting ofindividual, closely related vapors, such as alcohols from theirhomologous series and water vapor as an interferent. The tested sensingfilm made of octanethiol-capped Au nanoparticles was applied onto asensor by drop casting. The structures of alcohols 60 are illustrated inFIG. 9. Results of selectivity evaluation of the sensor are alsoillustrated where a single sensor discriminates between water vapor andindividual nine alcohol vapors from their homologous series, as shown bythe PCA scores plot 62. Measurements were performed with concentrationsof all vapors at 0, 0.089, 0.178, 0.267, and 0.356 P/P_(o). No previousindividual sensor has been reported to achieve this level of vapordiscrimination, while this discrimination was achieved here with asingle sensor.

EXAMPLE 6 Rejection of 300,000-1,900,000-Fold Overloading From WaterVapor

The sensor described in EXAMPLE 5 was further tested for rejection ofwater vapor interference from measured multivariable sensor response oftwo polar model analytes (1-octanol and 1-nonanol). The sensor responseto analyte vapors in mixtures with water vapor was corrected usingmultivariate analysis. FIG. 10 illustrates correlation plots 64 and 66between the actual and predicted concentrations of 1-octanol (plot 64)and 1-nonanol (plot 66). Predicted concentrations of these vapors in thepresence of different levels of humidity (ranging from 0 to 16,842 ppm)were calculated using multivariate analysis. The ratio of water vaporconcentration to the detection limit concentration of the analyte inthis mixture provided values of rejected water vapor overloading. TheTables of FIG. 11 summarize these findings for 1-octanol (Table 68) and1-nonanol (Table 70). This data shows that a single sensor rejected upto 1,900,000-fold overloading from water vapor when measuring analytevapors concentrations down to ppb levels.

EXAMPLE 7 Highly Selective Multivariate Vapor Sensing

In another experiment, the sensing material used to coat the sensor wasPEUT dissolved in a nonpolar solvent such as dichloromethane. During theexperiment, the sensor was incrementally exposed to three vapors, water,toluene, and THF, over a period of time. Concentrations of each vaporwere 0.18, 0.36, 0.53, and 0.71 P/Po. Dielectric constants of theseanalytes were 79 (water), 2.4 (toluene), and 7.5 (THF). As demonstratedand illustrated in FIGS. 12-14, a single passive sensor withmultivariable response was able to easily discriminate between thesethree vapors. Specifically, the individual Fp, F1, F2, Fz, Zp, Z1 and Z2responses, illustrated in plots 72 and 74 of FIG. 12, were analyzedusing PCA tools. The relations between Fp and Zp are illustrated in plot76 of FIG. 13, and the relations between calculated sensor resistance Rand calculated sensor capacitance C are illustrated in plot 78. Theplots 76 and 78 of FIG. 13 demonstrated a poor selectivity between waterand THF vapors, as indicated by the closely positioned curves of waterand THF responses. This poor selectivity between water and THF wasbecause the dielectric constant of the polyetherurethane sensing film(ε′_(r)=4.8) is lower than water or THF but is higher than toluene. Incontrast, the relationship between PC1 and PC2, illustrated in PCAscores plot 80 of FIG. 14, and the relationship between PC1, PC2, andPC3, illustrated in PCA scores plot 82 of FIG. 14, show a significantimprovement in selectivity of sensor when data is analyzed usingmultivariate analysis tools. The responses for all three vapors areroughly pointing into three different directions in the 2D plot 80.Importantly, the responses PC1, PC2, and PC3 of plot 82 are in a 3Ddirectional space demonstrating that the sensor performs as amultivariable device with three dimensions of response. Other sensors,for example, individual resistance and capacitance sensors produce onlya single response per sensor. Even a combination of individualresistance and capacitance sensors produces only two responses per thiscombination, one response per sensor. Thus, a single multivariableresponse wireless sensor, in accordance with embodiments of theinvention, reliably discriminated between these three example vaporsusing a “classic” polyetherurethane polymer.

EXAMPLE 8 Demonstration of Independent Contact Resistance and ContactCapacitance Responses

In another experiment, the sensing material used to coat the sensor wasoctanethiol-capped Au nanoparticles (Sigma Aldrich # 6604426) mixed withzinc 1,4,8,11,15,18,22,25-octabutoxy-29H,31H-phthalocyanine (SigmaAldrich # 383813) applied onto a sensor by drop casting. During theexperiment, the sensor was exposed to three vapors: acetone (vapor 1),acetonitrile (vapor 2), and toluene (vapor 3). Exposures were performedin a dynamic fashion where a vapor concentration was modulated with asinusoidal function where its period was first reducing during theexperiment and then increasing during the experiment. FIG. 15illustrates Fp, F1, F2, and Fz dynamic responses to the three vapors, asindicated by plots 84, 86, 88 and 90, respectively. FIG. 16 illustratesZp, Z1, and Z2 dynamic responses to the three vapors, as indicated byplots 92, 94 and 96, respectively.

This experiment was performed to explore if contributions to the vaporresponses arise from the same or from different portions of thetransducer circuit. If the contributions to the vapor responses arisefrom the same portion of the transducer circuit, then dynamic responsesrelated to frequency shifts Fp, F1, F2, and Fz should perfectly trackeach other and dynamic responses related to impedance change Zp, Z1, andZ2 should also perfectly track each other. However, if the contributionsto the vapor responses are arising from different portions of thetransducer circuit, then dynamic responses related to Fp, F1, F2, and Fzfrequency shifts and dynamic responses related to impedance change Zp,Z1, and Z2 could be different. Furthermore, these differences can bepresent or absent depending on the nature of vapor because of thedifferent types of interactions of different vapors with the sensingmaterial and the material of the electrodes of the transducer.

The modulation of vapor concentration with a variable period providesthe ability to evaluate the dynamics of the response to the vapor andthe recovery from the vapor exposure. If the response of the sensingmaterial to the vapor is faster than the smallest modulation period ofthe vapor concentration, then the amplitude of the sensor response willbe unchanged with an increased speed of vapor concentration change.However, if the response of the sensing material to the vapor is slowerthan a predetermined modulation period of the vapor concentration, thenthe amplitude of the sensor response will start decreasing with anincreased speed of vapor concentration change.

Similarly, if the recovery of the sensing material upon vapor exposureis faster than the smallest modulation period of the vaporconcentration, then the amplitude of the sensor recovery will beunchanged with an increased speed of vapor concentration change.However, if the recovery of the sensing material upon vapor exposure isslower than the smallest modulation period of the vapor concentration,then the amplitude of the sensor recovery will start decreasing with anincreased speed of vapor concentration change.

Thus, differences in the modulation of the amplitude of the sensorresponse and recovery should signify the existence of the contributionsto the transducer performance that are arising from the differentportions of the transducer circuit. These different portions of thetransducer circuit can produce an additional diversity in sensorresponse and can provide the ability of the individual sensor to detectmultiple vapors with high selectivity. This ability of the resonanttransducer is completely different from a simple combination ofindividual resistance and capacitance sensors that produce only tworesponses per combination, one response per sensor.

Referring to FIGS. 15-18, FIG. 15 illustrates Fp, F1, F2, and Fz dynamicresponses to three vapors, and FIG. 16 illustrates Zp, Z1, and Z2dynamic responses to three vapors such as acetone (vapor 1),acetonitrile (vapor 2), and toluene (vapor 3). The response and recoveryamplitudes of vapors 1, 2, and 3 show diverse profiles. These diverseprofiles are illustrated again in plots 98 and 100 of FIG. 17 fordynamic responses F1 and F2 to vapor 3 and in plots 102 and 104 for FIG.18 for dynamic responses Z1 and Z2 to vapor 1. FIG. 17 demonstrates thatvapor 3 affects different capacitance components of the transducercircuit (related to frequency changes in the LCR circuit) as evidencedby the differences in dynamic response and recovery profiles to vapor 3.FIG. 18 demonstrates that vapor 1 affects different resistancecomponents of the transducer circuit (related to resistance changes inan LCR circuit) as evidenced by the differences in dynamic response andrecovery profiles to vapor 1.

EXAMPLE 9 Highly Sensitive Multivariate Vapor Sensing

While impedance spectroscopy is a classic technique to characterizefundamental materials properties, its well-accepted limitations inpractical sensors for trace analyte detection include relatively lowsensitivity and prohibitively long acquisition times over the broadfrequency range. Thus, in order to enhance the ability to measurechanges in properties of the sensing material, the sensing material wasdeposited onto the electrodes of the resonant LCR sensor circuit.Similarly, this placement of the electrodes enhanced the ability tomeasure changes in properties of the fluid in proximity to the theelectrodes of the resonant LCR sensor circuit.

In further experiments, effects of changing dielectric constant onsensing electrodes both with and without a resonator were tested. Forexample, fluids of five different dielectric constants (mixtures ofwater and ethanol at different ratios) were flowed into the cell and theimpedance response of the sensor was monitored. The sensing ability ofresonant sensors was compared with the sensing ability of resonant LCRsensors. In these comparisons, the signal-to-noise (SNR) and detectionlimit (DL) from two measurement configurations were determined FIG. 19illustrates results of validation experiments with solutions of ε=25-80dielectric constants where results of phase shift measurements of animpedance spectrum and the peak shift of the resonance of the sensorwere compared. Specifically, a comparison of conventional impedancespectroscopy and resonant sensing using solutions of differentdielectric constants are illustrated in plots 106 and 108. Plot 106illustrates the sensor response (phase shift) measured usingconventional impedance spectroscopy. Plot 108 illustrates the sensorresponse (frequency shift) measured using a resonance sensor structure.Five different dielectric constants ε ranging from 25 to 80 wereproduced using solutions with different water/ethanol ratios, asillustrated in plots 106 and 108.

From the analysis of the collected data, it was observed thatconventional impedance spectroscopy measurements have a much largerrelative noise in the signal, as illustrated by plot 110 of FIG. 20. Toevaluate the SNR of sensor response, data was processed, as shown inplots 112 (frequency shift response of the LCR sensor) and 114 (phaseshift response of conventional impedance spectroscopy sensor) of FIG.20. Compared to the conventional impedance spectroscopy (plot 114), theresonant LCR sensor (plot 112) provided an at least 100-fold enhancementin the SNR over the smallest measured range of Ac with the correspondingimprovement of detection limit of dielectric constant determinations.

EXAMPLE 10 Improvement of Selectivity of Sensing of Vapors of SameDielectric Constant Using Power Modulation

Embodiments of the invention also provide the ability to discriminatebetween vapors of similar dielectric constant at room temperature, asillustrated in the experimental data of FIGS. 21-25. The selected vaporsfor this experiment were 1-pentanol (vapor 1), paraldehyde (vapor 2),and salicylaldehyde (vapor 3). The discrimination was achieved usingpower modulation. An interdigital chip served as a complementary sensorthat was attached across an antenna of a passive RFID tag. The chip was2×2 mm² and had gold electrodes that were 10 μm wide and 10 μm spacedfrom each other. A sensing film made of octanethiol-capped Aunanoparticles, manufactured by Sigma-Aldrich®, product # 660426, wasapplied onto a sensor chip by drop casting. The power of operation ofthe RFID sensor was controlled from −10 dBm to 0 dBm.

Plots 116 and 118 of FIG. 21 illustrate individual Fp, F1, F2, Fz, Zp,Z1 and Z2 responses upon a −10 dBm excitation. Results of PCA analysisof these responses is illustrated in FIG. 22. Plots 120 and 122 show thefirst two principal components as a function of measurement time. Plot124 shows the first two principal components demonstrating thedifficulties in discriminating between vapors 1 and 2.

Plots 126 and 128 of FIG. 23 illustrate individual Fp, F1, F2, Fz, Zp,Z1 and Z2 responses upon a 0 dBm excitation. Results of PCA analysis ofthese responses is illustrated in FIG. 24. Plots 130 and 132 show of thefirst two principal components as a function of measurement time. Plot134 shows the first two principal components demonstrating thediscrimination between vapors 1, 2, and 3.

FIG. 25 illustrates examples of changes of the resonance impedancespectral profiles of the passive RFID sensor upon −10 and 0 dBmexcitation when the sensor was exposed to a blank gas without testedvapors and to vapors 1, 2, and 3. Plots 136 and 138 of FIG. 25illustrate the changes in the real part of the resonance impedancespectra Z_(re)(ƒ) at −10 and 0 dBm excitation, respectively. Toillustrate the details of the spectral changes, the spectra of sensorresponse to vapors 1, 2, and 3 were normalized by the relevant spectraZ^(o) _(re)(ƒ) of the sensor exposed to a blank gas without testedvapors. Plots 140 and 142 illustrate these normalized spectraZ_(re)/Z^(o) _(re) at −10 and 0 dBm excitation, respectively. Plots 140and 142 conclusively demonstrate that the vapor-induced resonanceimpedance is significantly changed upon changes in the excitation powerfrom −10 dBm to 0 dBm. These changes provide the ability to discriminatebetween all three vapors at an appropriately selected level of excitaionpower for the RFID sensor as shown in plot 134 of FIG. 24.

While only certain features of the invention have been illustrated anddescribed herein, many modifications and changes will occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all such modifications and changes as fallwithin the true spirit of the invention.

1. A method of detecting analytes in a fluid, comprising: acquiring animpedance spectrum over a resonant frequency range of a single resonantsensor circuit; and calculating a multivariate signature from theacquired impedance spectrum.
 2. The method, as set forth in claim 1,wherein calculating a multivariate signature from the acquired impedancespectrum is performed using the real part of the impedance spectrum. 3.The method, as set forth in claim 1, wherein the resonant frequencyrange is within a range of 0.1 Hz-1000 THz.
 4. The method, as set forthin claim 1, wherein acquiring the impedance spectrum is performed in afluid having greater than 50% humidity.
 5. The method, as set forth inclaim 1, wherein the multivariate signature is calculated usingprincipal components analysis, canonical correlation analysis,regression analysis, nonlinear regression analysis, discriminatefunction analysis, multidimensional scaling, linear discriminateanalysis, logistic regression, or neural network analysis.
 6. Themethod, as set forth in claim 1, wherein acquiring the impedancespectrum comprises detecting dielectric, dimensional, resistance, chargetransfer or other changes of material properties by monitoring changesin properties of the resonant circuit.
 7. The method, as set forth inclaim 1, wherein the resonant circuit comprises aninductor-capacitor-resistor (LCR) circuit.
 8. The method, as set forthin claim 1, comprising: measuring a real part and an imaginary part ofthe impedance spectrum of a resonant sensor antenna of the singleresonant sensor circuit, wherein the resonant sensor antenna is coatedwith a sensing material; calculating at least six spectral parameters ofthe single resonant sensor circuit coated with the sensing material;reducing the impedance spectrum to a single data point usingmultivariate analysis to selectively identify the analyte; anddetermining one or more environmental parameters from the impedancespectrum.
 9. The method, as set forth in claim 1, comprising: poweringthe single resonant sensor circuit to at least two power levels toaffect at least one of the dipole moment, the dielectric constant, andthe temperature of a sensing material disposed on the single resonantsensor circuit; collecting spectral parameters of a sensor response ofthe single resonant sensor circuit at the at least two power levels;performing multivariate analysis of the spectral parameters fromcombined impedance spectral profiles of the single resonant sensorcircuit at the different power levels; and calculating values ofenvironmental parameters to which the single resonant sensor circuit isexposed from data produced by performing the multivariate analysis
 10. Amethod of detecting chemical or biological species in a fluid,comprising: measuring a real part and an imaginary part of an impedancespectrum of a single resonant sensor antenna coated with a sensingmaterial; calculating at least six spectral parameters of the singleresonant sensor antenna coated with the sensing material; reducing theimpedance spectrum to a single data point using multivariate analysis toselectively identify an analyte; and determining one or moreenvironmental parameters from the impedance spectrum.
 11. The method, asset forth in claim 10, wherein measuring the impedance spectrum andcalculating at least six spectral parameters comprises measuring over aresonant frequency range of the single resonant sensor antenna.
 12. Themethod, as set forth in claim 10, wherein calculating at least sixspectral parameters comprises calculating a frequency position of thereal part of the impedance spectrum, and a magnitude of the real part ofthe impedance spectrum.
 13. The method, as set forth in claim 10,wherein calculating at least six spectral parameters comprisescalculating a resonant frequency of the imaginary part of the impedancespectrum, and an anti-resonant frequency of the imaginary part of theimpedance spectrum.
 14. The method, as set forth in claim 10, whereindetermining comprises determining a resistance and a capacitance of thesingle resonant sensor antenna coated with a sensing material.
 15. Themethod, as set forth in claim 10, wherein reducing the impedancespectrum to a single data point comprises calculating a multivariatesignature.
 16. The method, as set forth in claim 10, comprising:acquiring the impedance spectrum over a resonant frequency range of aresonant sensor circuit having the single resonant sensor antenna; andcalculating a multivariate signature from the acquired impedancespectrum.
 17. The method, as set forth in claim 10, comprising: poweringan IC chip having the single resonant sensor antenna to at least onepower level to affect an impedance spectral profile of a sensor havingthe IC chip; collecting spectral parameters of a sensor response of thesensor at the at least one power level; performing multivariate analysisof the spectral parameters; and calculating values of environmentalparameters to which the sensor is exposed from data produced byperforming the multivariate analysis and using stored calibrationcoefficients.
 18. A method for controlling selectivity of a sensorresponse of a sensor having a single integrated circuit (IC) chip,comprising: powering the single IC chip to at least one power level toaffect an impedance spectral profile of the sensor; collecting spectralparameters of the sensor response at the at least one power level;performing multivariate analysis of the spectral parameters; andcalculating values of environmental parameters to which the sensor isexposed from data produced by performing the multivariate analysis andusing stored calibration coefficients.
 19. The method, as set forth inclaim 18, wherein powering the single IC chip comprises powering thesingle IC chip to at least one power level in the range from −50 dBm to+40 dBm.
 20. The method, as set forth in claim 18, further comprisingcalculating a concentration of at least one analyte in the presence ofat least one interferent.
 21. The method, as set forth in claim 18,wherein performing the multivariate analysis of spectral parameterscomprises principal components analysis, canonical correlation analysis,regression analysis, nonlinear regression analysis, discriminatefunction analysis, multidimensional scaling, linear discriminateanalysis, logistic regression, or neural network analysis.
 22. Themethod, as set forth in claim 18, wherein powering the single IC chipcomprises powering the single IC chip to at least two different powerlevels and wherein performing multivariate analysis comprises performingmultivariate analysis of the spectral parameters from combined impedancespectral profiles of the sensor at the at least two different powerlevels.
 23. The method, as set forth in claim 22, wherein the impedancespectral profiles are different at each of the at least two differentpower levels.
 24. The method, as set forth in claim 18, wherein poweringthe single IC chip comprises powering the single IC chip to at least twodifferent power levels and wherein performing multivariate analysiscomprises performing multivariate analysis of the spectral parameters,wherein differences in the spectral profiles at each of the at least twodifferent power levels are pronounced in different values of Fp, F1, F2,Fz, Zp, Z1, Z2, and calculated values of C and R.
 25. The method, as setforth in claim 18, further comprising calculating concentration of atleast one analyte in the presence of at least one interferent.
 26. Themethod, as set forth in claim 18, comprising: acquiring an impedancespectrum over a resonant frequency range of the single IC chip; andcalculating a multivariate signature from the acquired impedancespectrum.
 27. The method, as set forth in claim 18, comprising:measuring a real part and an imaginary part of the impedance spectrum ofa resonant sensor antenna of the single IC chip, wherein the resonantsensor antenna is coated with a sensing material; calculating at leastsix spectral parameters of the resonant sensor antenna coated with thesensing material; reducing the impedance spectrum to a single data pointusing multivariate analysis to selectively identify the analyte; anddetermining one or more environmental parameters from the impedancespectrum.